import math

import mindspore as ms

from mindspore import nn ,ops


def conv1x1(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, has_bias=False)


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,pad_mode="pad",
                     padding=1, has_bias=False)

class BasicBlock(nn.Cell):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes,momentum=0.1)
        self.relu = nn.ReLU()
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes,momentum=0.1)
        self.downsample = downsample
        self.stride = stride    

    def construct(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out    


class ResNet(nn.Cell):

    def __init__(self,block,layers):
        self.inplanes = 32
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3,32,kernel_size=3,stride=1,padding=1,has_bias=False,pad_mode="pad")
        
        self.bn1=nn.BatchNorm2d(32,momentum=0.1)
        self.relu = nn.ReLU()

        self.layer1 = self._make_layer(block,32,layers[0],stride=2)
        self.layer2 = self._make_layer(block, 64, layers[1], stride=1)
        self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 256, layers[3], stride=1)
        self.layer5 = self._make_layer(block, 512, layers[4], stride=1)

        for _, cell in self.cells_and_names():
            if isinstance(cell,nn.Conv2d):#初始化有问题，这个data.normal_是不是正态分布
                n = cell.kernel_size[0] *cell.kernel_size[1] * cell.out_channels
                cell.weight.set_data(ms.common.initializer.initializer(ms.common.initializer.Normal(sigma= math.sqrt(2. / n), mean=0),cell.weight.shape, cell.weight.dtype))   
            elif isinstance(cell,nn.BatchNorm2d):
                 cell.gamma.set_data(ms.common.initializer.initializer("ones", cell.gamma.shape, cell.gamma.dtype))
                 cell.beta.set_data(ms.common.initializer.initializer("zeros", cell.beta.shape, cell.beta.dtype))

    def _make_layer(self,block,planes,blocks,stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.SequentialCell(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, has_bias=False),
                nn.BatchNorm2d(planes * block.expansion,momentum=0.1),
            )

            layers=[]
            layers.append(block(self.inplanes, planes, stride, downsample))
            self.inplanes = planes * block.expansion
            for i in range(1, blocks):
                layers.append(block(self.inplanes, planes))

            return nn.SequentialCell(*layers)            
            
    def construct(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        #print(x.shape)
        return x


def resnet45():
    return ResNet(BasicBlock, [3, 4, 6, 6, 3])


